IDEA Spark Streaming Kafka数据源-Consumer

import org.apache.spark.SparkConf
import org.apache.spark.streaming.kafka.KafkaUtils
import org.apache.spark.streaming.{Minutes, Seconds, StreamingContext}
object DStream_Kafa_Consumer {
  def main(args: Array[String]): Unit = {

    val sc=new SparkConf().setAppName("kafaCountWord").setMaster("local[2]")
    val ssc=new StreamingContext(sc,Seconds(10))
    ssc.checkpoint("file:///usr/local2/spark/mycode/kafa/checkpoint")
    val zookeeperServer="localhost:2181"//zookeeper服务器地址(默认的)
    val group="soyo_kafa_group"
    val topics="wordsender"
    val num=1  //每个topic的分区数
    val topicMap=topics.split(",").map((_,num.toInt)).toMap
    val lines=KafkaUtils.createStream(ssc,zookeeperServer,group,topicMap).map(_._2)

    val words=lines.flatMap(_.split(" "))
   val wordCounts=words.map(x=>(x,1)).reduceByKeyAndWindow(_+_,_-_,Minutes(2),Seconds(10),2)
  // val wordCounts_2=words.map(x=>(x,1)).reduceByKey(_+_)
    wordCounts.print(200)
   // wordCounts_2.print(200)
    ssc.start()
    ssc.awaitTermination

  }

}

结果：需要Kafka-Producer程序输入数据

-------------------------------------------
Time: 1508230980000 ms
-------------------------------------------
(4,61)
(8,69)
(6,66)
(0,70)
(2,61)
(7,69)
(5,61)
(9,74)
(3,62)
(1,72)

-------------------------------------------
Time: 1508230990000 ms
-------------------------------------------
(4,61)
(8,69)
(6,66)
(0,70)
(2,61)
(7,69)
(5,61)
(9,74)
(3,62)
(1,72)

-------------------------------------------